def save(idx, gSaver, dSaver):
print("start save")
saveToFile = ConvNet.openBinaryFileW("gan11g"+str(idx)+".bin")
for item in gSaver:
item(saveToFile)
saveToFile.flush();saveToFile.close()
saveToFile = ConvNet.openBinaryFileW("gan11d"+str(idx)+".bin")
for item in dSaver:
item(saveToFile)
saveToFile.flush();saveToFile.close()
print("end save")
def train():
#with tf.Session(config=tf.ConfigProto(device_count = {'GPU': 0})) as sess:
with tf.Session() as sess:
#初始化参数
sess.run(tf.global_variables_initializer())
for j in xrange(0, 10):
#打印当前网络的输出值
accurate = 0
for _i in xrange(0,100):
testData = MNISTDataLargeSet.extract_data(1)
testLabel = MNISTDataLargeSet.extract_label(1,onehot = True)
lbl = sess.run(_test, feed_dict={testOne:testData})
if np.argmax(lbl, 1) == np.argmax(testLabel, 1):
accurate = accurate + 0.01
#执行训练
totalLoss = 0.0
for _i in xrange(0,100):
trainData = MNISTDataLargeSet.extract_data(BATCH_SIZE)
trainLabel = MNISTDataLargeSet.extract_label(BATCH_SIZE,onehot = True)
_,_loss = sess.run([optimizer,loss], feed_dict={labels_node: trainLabel, inputlayer: trainData})
totalLoss = totalLoss + _loss
print(j,"accu:",accurate,"loss:",totalLoss)
#保存已训练的网络
Saver = []
for item in plist:
Saver.append(item.getSaver(sess, True))
saveToFile = ConvNet.openBinaryFileW("MNISTLargeSet.bin")
for item in Saver:
item(saveToFile)
saveToFile.flush();saveToFile.close()